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Relational Learning for Personalized Medicine

Monday, March 17, 2014 -

10:00am to 11:00am

KEC 1007

Speaker Information

Sriraam Natarajan

Assistant Professor

School of Informatics and Computing

Indiana University

Abstract

Recent advances in medicine and electronic book-keeping have greatly increased the amount of medical data available for research andclinical decision making. Electronic Health Records include information about test results, lab reports, medical images, genomics,treatments, outcomes, and family histories. Together with recent advances in data mining and machine learning, it now seems possible to realize the grand vision of predictive personalized medicine. Statistical Relational Learning (SRL) combines the powerful formalisms of probability theory and first-order logic to handle uncertainty in large, complex problems. In this talk, I illustrate the potential ofSRL to achieve an important sub-goal of predictive medicine: early detection. Specifically, I will present SRL approaches for (1)identifying young adults who are at high risk of developing Coronary Heart Disease in middle and later life, and (2) identifying the set ofpatients who have or will have Alzheimer's Disease by analyzing their brain MRI images. I will present a general approach for learning SRL models based on Functional-Gradient Boosting. I will adapt this algorithm for the above mentioned challenging tasks to produce state-of-the-art results in three real-world medical studies. I will outline other interesting problems in personalized medicine that we are addressing using SRL and conclude on the optimistic note that predictive personalized medicine is within reach in the near future.

Speaker Bio

SriraamNatarajan is an Assistant professor at Indiana University, USA. He was previously an Assistant Professor at Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Bio-Medical Applications. He has received the Young Investigator award from US Army Research Office. He has served on the PC of several conferences/workshops such as AAAI, IJCAI, ICML, ILP and SRL. He co-organized the AAAI 2010, the UAI 2012 and AAAI 2013 workshops on Statistical Relational AI (StarAI), ICML 2012 Workshop on Statistical Relational Learning, and the ECMLPKDD 2011 and 2012 workshops on Collective Learning and Inference on Structured Data (Co-LISD). He will serve as the co-chair of the AAAI student abstract and posters at AAAI 2014.